LibGuides: Research Data Management Guide: Definitions

Terms and definitions

Data deposit: Refers to when the research data collected as part of a research project are transferred to a research data repository.

Data: Data are facts, measurements, recordings, records, or observations collected by researchers and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of text, numbers, symbols, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, workflows, equipment descriptions, data files, data processing algorithms, software, programming languages, code, or statistical records.

Research data: Research data are data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or creative practice, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. What is considered relevant research data is often highly contextual, and determining what counts as such should be guided by disciplinary norms.

Research data management (RDM): Research data management refers to the processes applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data.

Metadata: Metadata refers to the data or information that supports the discovery, understanding, and management of your research data. Standards for metadata vary from one discipline to another, but they usually indicate who created the data, when and how they were created, their quality, accuracy, and precision, as well as other characteristics necessary to the discovery, understanding, and reuse of the data.

Data management plan (DMP): A data management plan is a living document, typically associated with an individual research project or program that consists of the practices, processes and strategies that pertain to a set of specified topics related to data management and curation. DMPs should be modified throughout the course of a research project to reflect changes in project design, methods, or other considerations.

FAIR principles: FAIR principles for scientific data management and stewardship are an international best practice for improving the findability, accessibility, interoperability and reuse of digital assets.

Indigenous data sovereignty: Refers to data related to research by and with Indigenous communities that must be managed in accordance with data management principles developed and approved by these communities.

Institutional research data management strategy: An institutional RDM strategy describes how the institution will provide its researchers with an environment that enables and supports RDM practices. Developing these strategies will help research institutions identify and address gaps and challenges in infrastructure, resources and practices related to RDM. Each strategy should reflect the institution’s particular circumstances, including the institution’s size and capacity, geography, and other contextual factors.

Tri-Agency, the agencies: The three federal granting agencies: Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC).